# Transfer Learning for Underrepresented Music Generation

arXiv: https://arxiv.org/abs/2306.00281 · HTML: https://mlsanity.com/paper/2306.00281/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music](https://mlsanity.com/paper/1803.05428/) (2018)
- [MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation](https://mlsanity.com/paper/1703.10847/) (2017)
- [A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions](https://mlsanity.com/paper/2011.06801/) (2020)
- [Deep Learning Techniques for Music Generation -- A Survey](https://mlsanity.com/paper/1709.01620/) (2017)
- [Conceptual Expansion Neural Architecture Search (CENAS)](https://mlsanity.com/paper/2110.03144/) (2021)

## Built on by (most cited first, 1 of 1)

- [Missing Melodies: AI Music Generation and its "Nearly" Complete Omission of the Global South](https://mlsanity.com/paper/2412.04100/) (2024)

## Abstract (arXiv)

This paper investigates a combinational creativity approach to transfer learning to improve the performance of deep neural network-based models for music generation on out-of-distribution (OOD) genres. We identify Iranian folk music as an example of such an OOD genre for MusicVAE, a large generative music model. We find that a combinational creativity transfer learning approach can efficiently adapt MusicVAE to an Iranian folk music dataset, indicating potential for generating underrepresented music genres in the future.